Vehicle platoon following deciding system based on cloud computing and deciding method thereof

ABSTRACT

A vehicle platoon following deciding system based on cloud computing is configured to decide a plurality of vehicle platoon accelerations of a leading vehicle and at least one following vehicle. A cloud processing unit receives a leading vehicle parameter group and at least one following vehicle parameter group. The cloud processing unit is configured to implement a cloud deciding step. The cloud deciding step includes judging whether the leading vehicle is manually driven according to the leading vehicle parameter group to generate a driving mode judging result, calculating a driving acceleration range according to a leading vehicle acceleration range and at least one following vehicle acceleration range, estimating a compensated acceleration according to the leading vehicle parameter group, and calculating the vehicle platoon accelerations according to the driving mode judging result and at least one of the driving acceleration range and the compensated acceleration.

BACKGROUND Technical Field

The present disclosure relates to a vehicle platoon following decidingsystem and a deciding method thereof. More particularly, the presentdisclosure relates to a vehicle platoon following deciding system basedon cloud computing and a deciding method thereof.

Description of Related Art

No matter what the field of logistics and freight transportation ortransport, man-hours and manpower allocation are importantconsiderations of operating costs. If a plurality of vehicles haveautonomous capability with vehicle platoon following, they caneffectively improve operation and carrying efficiency. Because the useof autonomous vehicle platoon can reduce the need for manpower, andcommercial transport has relatively simple application scenes, many carmanufacturers have invested in the development of autonomous vehiclesplatoon and hope to achieve commercial autonomous vehicle platoonfollowing as soon as possible.

Conventional vehicle platoon following deciding technologies may bedivided into several types. A first type is the error correction of thevehicle platoon according to a current path and a predetermined path ofthe front vehicle. A second type is the position correction of the rearvehicle according to a vehicle centerline, an angle and a lanecenterline of the front vehicle. A third type is the control decision ofthe vehicle platoon when a single obstacle cut-in front of the rearvehicle. However, the above types still encounter many bottlenecks andproblems when applied to actual vehicle platoon following. For example,a delay time of each vehicle in the vehicle platoon is not considered ina longitudinal direction, the rationality of an acceleration of eachvehicle is not considered when the each vehicle is switched between amanual mode and an autonomous mode, and a lack of a reasonable decisionfor multiple obstacles cut-in the vehicle platoon. Therefore, a vehicleplatoon following deciding system based on cloud computing and adeciding method thereof which are capable of realizing a cloudintegrated decision, a delay self-diagnosis, the reasonable decision formultiple obstacles cut-in the vehicle platoon and adaptively adjusting areasonable acceleration of the each vehicle are commercially desirable.

SUMMARY

According to one aspect of the present disclosure, a vehicle platoonfollowing deciding system based on cloud computing is configured todecide a plurality of vehicle platoon accelerations of a leading vehicleand at least one following vehicle, and includes a leading vehicleprocessing unit, at least one following vehicle processing unit and acloud processing unit. The leading vehicle processing unit is disposedon the leading vehicle and configured to generate a leading vehicleparameter group. The leading vehicle parameter group includes a leadingvehicle acceleration range. The at least one following vehicleprocessing unit is disposed on the at least one following vehicle andconfigured to generate at least one following vehicle parameter group.The at least one following vehicle parameter group includes at least onefollowing vehicle acceleration range. The cloud processing unit issignally connected to the leading vehicle processing unit and the atleast one following vehicle processing unit, and receives the leadingvehicle parameter group and the at least one following vehicle parametergroup. The cloud processing unit is configured to implement a clouddeciding step including performing a driving mode judging step, a cloudparameter uniformizing step, a cloud acceleration estimating step and avehicle platoon acceleration calculating step. The driving mode judgingstep is performed to judge whether the leading vehicle is manuallydriven according to the leading vehicle parameter group to generate adriving mode judging result. The cloud parameter uniformizing stepincludes calculating a driving acceleration range according to theleading vehicle acceleration range and the at least one followingvehicle acceleration range. The cloud acceleration estimating step isperformed to estimate a compensated acceleration according to theleading vehicle parameter group. The vehicle platoon accelerationcalculating step is performed to calculate the vehicle platoonaccelerations according to the driving mode judging result and at leastone of the driving acceleration range and the compensated acceleration.

According to another aspect of the present disclosure, a vehicle platoonfollowing deciding system based on cloud computing is configured todecide a plurality of vehicle platoon accelerations of a leading vehicleand at least one following vehicle. The vehicle platoon followingdeciding system based on cloud computing includes a leading vehicleprocessing unit, at least one following vehicle processing unit and acloud processing unit. The leading vehicle processing unit is disposedon the leading vehicle and configured to generate a leading vehicleparameter group. The leading vehicle parameter group includes a leadingvehicle acceleration range. The at least one following vehicleprocessing unit is disposed on the at least one following vehicle andconfigured to generate at least one following vehicle parameter group.The at least one following vehicle parameter group includes at least onefollowing vehicle acceleration range. The cloud processing unit issignally connected to the leading vehicle processing unit and the atleast one following vehicle processing unit, and receives the leadingvehicle parameter group and the at least one following vehicle parametergroup. The cloud processing unit is configured to implement a clouddeciding step including generating a driving mode judging result, adriving acceleration range and a compensated acceleration according tothe leading vehicle parameter group, the leading vehicle accelerationrange and the at least one following vehicle acceleration range, andthen calculating the vehicle platoon accelerations according to thedriving mode judging result and at least one of the driving accelerationrange and the compensated acceleration. One of the leading vehicleprocessing unit and the at least one following vehicle processing unitis configured to implement a delay diagnosing step. The delay diagnosingstep includes diagnosing whether a signal delay time between the cloudprocessing unit and the one of the leading vehicle processing unit andthe at least one following vehicle processing unit is smaller than orequal to a predetermined delay time to generate a delay diagnosisresult.

According to further another aspect of the present disclosure, adeciding method of a vehicle platoon following deciding system based oncloud computing is configured to decide a plurality of vehicle platoonaccelerations of a leading vehicle and at least one following vehicle.The deciding method of the vehicle platoon following deciding systembased on cloud computing includes performing a cloud deciding step. Thecloud deciding step includes performing a driving mode judging step, acloud parameter uniformizing step, a cloud acceleration estimating stepand a vehicle platoon acceleration calculating step. The driving modejudging step is performed to configure a cloud processing unit to judgewhether the leading vehicle is manually driven according to a leadingvehicle parameter group to generate a driving mode judging result. Thecloud parameter uniformizing step includes configuring the cloudprocessing unit to calculate a driving acceleration range according tothe leading vehicle acceleration range and the at least one followingvehicle acceleration range. The cloud acceleration estimating step isperformed to configure the cloud processing unit to estimate acompensated acceleration according to the leading vehicle parametergroup. The vehicle platoon acceleration calculating step is performed toconfigure the cloud processing unit to calculate the vehicle platoonaccelerations according to the driving mode judging result and at leastone of the driving acceleration range and the compensated acceleration.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure can be more fully understood by reading thefollowing detailed description of the embodiment, with reference made tothe accompanying drawings as follows:

FIG. 1 shows a schematic view of a vehicle platoon following decidingsystem based on cloud computing according to a first embodiment of thepresent disclosure.

FIG. 2 shows a partial block diagram of the vehicle platoon followingdeciding system based on cloud computing of FIG. 1 .

FIG. 3 shows a schematic view of a cloud deciding step of the vehicleplatoon following deciding system based on cloud computing of FIG. 1 .

FIG. 4 shows a flow chart of a driving mode judging step, a cloudparameter uniformizing step, a cloud acceleration estimating step and avehicle platoon acceleration calculating step of the cloud deciding stepof FIG. 3 .

FIG. 5 shows a flow chart of the cloud acceleration estimating step ofFIG. 4 .

FIG. 6 shows a schematic view of a vehicle platoon following decidingsystem based on cloud computing according to a second embodiment of thepresent disclosure.

FIG. 7 shows a schematic view of a signal delay time of one of aplurality of delay diagnosing steps of FIG. 6 , corresponding to asignal path.

FIG. 8 shows a flow chart of estimation of the signal delay time of theone of the delay diagnosing steps of FIG. 6 .

FIG. 9 shows a flow chart of a deciding method of a vehicle platoonfollowing deciding system based on cloud computing according to a thirdembodiment of the present disclosure.

FIG. 10 shows a flow chart of a deciding method of a vehicle platoonfollowing deciding system based on cloud computing according to a fourthembodiment of the present disclosure.

FIG. 11 shows a schematic view of a vehicle platoon following decidingsystem based on cloud computing for processing a scene with an obstaclecut-in according to a fifth embodiment of the present disclosure.

FIG. 12 shows a flow chart of a deciding method of a vehicle platoonfollowing deciding system based on cloud computing for processing ascene with an obstacle cut-in according to a sixth embodiment of thepresent disclosure.

DETAILED DESCRIPTION

The embodiment will be described with the drawings. For clarity, somepractical details will be described below. However, it should be notedthat the present disclosure should not be limited by the practicaldetails, that is, in some embodiment, the practical details isunnecessary. In addition, for simplifying the drawings, someconventional structures and elements will be simply illustrated, andrepeated elements may be represented by the same labels.

It will be understood that when an element (or device) is referred to asbe “connected to” another element, it can be directly connected to theother element, or it can be indirectly connected to the other element,that is, intervening elements may be present. In contrast, when anelement is referred to as be “directly connected to” another element,there are no intervening elements present. In addition, the terms first,second, third, etc. are used herein to describe various elements orcomponents, these elements or components should not be limited by theseterms. Consequently, a first element or component discussed below couldbe termed a second element or component.

Please refer to FIGS. 1, 2, 3 and 4 . FIG. 1 shows a schematic view of avehicle platoon following deciding system 100 based on cloud computingaccording to a first embodiment of the present disclosure. FIG. 2 showsa partial block diagram of the vehicle platoon following deciding system100 based on cloud computing of FIG. 1 . FIG. 3 shows a schematic viewof a cloud deciding step S02 of the vehicle platoon following decidingsystem 100 based on cloud computing of FIG. 1 . FIG. 4 shows a flowchart of a driving mode judging step S022, a cloud parameteruniformizing step S024, a cloud acceleration estimating step S026 and avehicle platoon acceleration calculating step S028 of the cloud decidingstep S02 of FIG. 3 . The vehicle platoon following deciding system 100based on cloud computing is configured to decide a plurality of vehicleplatoon accelerations a_(f) of a leading vehicle 200 and at least onefollowing vehicle 300, and includes the leading vehicle 200, a leadingvehicle processing unit 210, a communicating device 220, a positioningdevice 230, a sensing device 240, the at least one following vehicle300, at least one following vehicle processing unit 310, a communicatingdevice 320, a positioning device 330, a sensing device 340 and a cloudcomputing platform 400.

The leading vehicle processing unit 210, the communicating device 220,the positioning device 230 and the sensing device 240 are disposed onthe leading vehicle 200. The leading vehicle processing unit 210 issignally connected to the communicating device 220, the positioningdevice 230 and the sensing device 240. The leading vehicle processingunit 210 is configured to generate a leading vehicle parameter group212, and the leading vehicle parameter group 212 includes a leadingvehicle acceleration range (a_(L) ^(i), a_(U) ^(i)), where i is equal to1, i.e., (a_(L) ¹, a_(U) ¹). L represents a minimum acceleration, and Urepresents a maximum acceleration. The communicating device 220 isconfigured to enable the leading vehicle processing unit 210 tocommunicate with the outside. The positioning device 230 is configuredto position the leading vehicle 200, such as a global positioning system(GPS). The sensing device 240 is configured to sense a surroundingenvironment of the leading vehicle 200. In addition, in one embodiment,the leading vehicle parameter group 212 includes a current coordinate, acommunication delay, a vehicle load, a chassis delay, a vehicle speed, avehicle acceleration, a percentage of throttle brake, a steering wheelangle, a current acceleration of a front vehicle, a vehicle speed limit,a road curvature and a road gradient. The vehicle acceleration includesthe leading vehicle acceleration range (a_(L) ¹, a_(U) ¹), but thepresent disclosure is not limited thereto.

The at least one following vehicle processing unit 310, thecommunicating device 320 and the positioning device 330 are disposed onthe at least one following vehicle 300. The at least one followingvehicle processing unit 310 is signally connected to the communicatingdevice 320 and the positioning device 330. The at least one followingvehicle processing unit 310 is configured to generate a followingvehicle parameter group 312, and the following vehicle parameter group312 includes at least one following vehicle acceleration range (a_(L)^(i), a_(U) ^(i)), where i is equal to one of 2 to N. N represents thetotal number of leading vehicle 200 and the at least one followingvehicle 300, and is a positive integer greater than or equal to 2, i.e.,the at least one following vehicle acceleration range (a_(L) ^(i), a_(U)^(i))=(a_(L) ², a_(U) ²)-(a_(L) ^(N), a_(U) ^(N)). The communicatingdevice 320 is configured to enable the at least one following vehicleprocessing unit 310 to communicate with the outside. The positioningdevice 330 is configured to position the at least one following vehicle300, such as GPS. The sensing device 340 may be optional. When thesensing device 340 is disposed on the at least one following vehicle300, the sensing device 340 is signally connected to the at least onefollowing vehicle processing unit 310 and configured to sense asurrounding environment of the at least one following vehicle 300. Inaddition, in one embodiment, the following vehicle parameter group 312includes a current coordinate, a communication delay, a vehicle load, achassis delay, a vehicle speed, a vehicle acceleration, a vehicle speedlimit, a road curvature and a road gradient. The vehicle accelerationincludes the at least one following vehicle acceleration range (a_(L) ²,a_(U) ²)-(a_(L) ^(N), a_(U) ^(N)), but the present disclosure is notlimited thereto.

The cloud computing platform 400 includes a cloud processing unit 410.The cloud processing unit 410 is signally connected to the leadingvehicle processing unit 210 and the at least one following vehicleprocessing unit 310, and receives the leading vehicle parameter group212 and the at least one following vehicle parameter group 312. Theleading vehicle processing unit 210 and the at least one followingvehicle processing unit 310 are signally connected to the cloudprocessing unit 410 by the communicating devices 220, 320, respectively.The cloud processing unit 410 is configured to implement a signalreceiving step S01 and a cloud deciding step S02. The signal receivingstep S01 is “Receiving vehicle request signal?”, and represents thatconfirming whether to receive a vehicle request signal. If yes,receiving a vehicle parameter group (e.g., the leading vehicle parametergroup 212 or the at least one following vehicle parameter group 312) andperforming the cloud deciding step S02. If no, performing the signalreceiving step S01 again. In addition, the cloud deciding step S02includes performing the driving mode judging step S022, the cloudparameter uniformizing step S024, the cloud acceleration estimating stepS026 and the vehicle platoon acceleration calculating step S028. Thedriving mode judging step S022 is performed to judge whether the leadingvehicle 200 is manually driven according to the leading vehicleparameter group 212 to generate a driving mode judging result. The cloudparameter uniformizing step S024 includes calculating a drivingacceleration range (a_(min) ^(f), a_(max) ^(f)) according to the leadingvehicle acceleration range (a_(L) ¹, a_(U) ¹) and the at least onefollowing vehicle acceleration range (a_(L) ², a_(U) ²)-(a_(L) ^(N),a_(U) ^(N)). The cloud acceleration estimating step S026 is performed toestimate a compensated acceleration a_(pa) according to the leadingvehicle parameter group 212. The vehicle platoon accelerationcalculating step S028 is performed to calculate the vehicle platoonaccelerations a_(f) according to the driving mode judging result and atleast one of the driving acceleration range (a_(min) ^(f), a_(max) ^(f))and the compensated acceleration a_(pa). Therefore, the vehicle platoonfollowing deciding system 100 based on cloud computing of the presentdisclosure utilizes the cloud parameter uniformizing step S024 and thecloud acceleration estimating step S026 to realize vehicle platoonfollowing decisions in different driving modes.

Please refer to FIGS. 1, 2, 3, 4 and 5 . FIG. 5 shows a flow chart ofthe cloud acceleration estimating step S026 of FIG. 4 . The driving modejudging step S022 is “Is leading vehicle manually driven?”, andrepresents that judging whether the leading vehicle 200 is manuallydriven according to the current coordinate, the communication delay, theroad curvature, the road gradient, the chassis delay, the vehicleacceleration and the vehicle load of the leading vehicle parameter group212 to generate the driving mode judging result. If yes, performing thecloud acceleration estimating step S026. If no, performing the cloudparameter uniformizing step S024. The driving mode judging result hastwo types of modes. One is that the leading vehicle 200 is in anautonomous mode, and the other is that the leading vehicle 200 is in amanual mode.

The cloud parameter uniformizing step S024 includes a plurality of stepsS0241, S0242 a, S0242 b, S0243 a, S0243 b, S0244 a, S0244 b, S0244 c,S0245. The step S0241 is “Uniformizing parameter”, and represents thatcalculating a uniformized acceleration range parameter (a_(min),a_(max)) according to the leading vehicle acceleration range (a_(L) ¹,a_(U) ¹) and the at least one following vehicle acceleration range(a_(L) ², a_(U) ²)-(a_(L) ^(N), a_(U) ^(N)). The uniformizedacceleration range parameter (a_(min), a_(max)) meets the followingequations (1) and (2):

a _(min)=max{a _(L) ¹ , . . . ,a _(L) ^(N)}  (1).

a _(max)=min{a _(U) ¹ , . . . ,a _(U) ^(N)}  (2).

max represents obtaining a maximum value, and min represents obtaining aminimum value. In addition, the leading vehicle processing unit 210 isconfigured to generate a leading vehicle control delay time t₁ ^(a) anda leading vehicle communication delay time t*_(com1). The at least onefollowing vehicle processing unit 310 is configured to generate at leastone following vehicle control delay time (t₂ ^(a)-t_(N) ^(a)) and atleast one following vehicle communication delay time t*_(com2). Theleading vehicle communication delay time t*_(com1) and the at least onefollowing vehicle communication delay time t*_(com2) meet the followingequations (3) and (4), respectively:

$\begin{matrix}{{t_{{com}1}^{*} = \begin{bmatrix}C_{L}^{i} \\C_{L}^{i}\end{bmatrix}},{i = 1.}} & (3)\end{matrix}$ $\begin{matrix}{{t_{{com}2}^{*} = \begin{bmatrix}C_{L}^{i} \\C_{U}^{i}\end{bmatrix}},{i = 2},\ldots,{N.}} & (4)\end{matrix}$

C represents a communication status. In addition, the step S0242 a is“Calculating vehicle platoon communication delay time”, and representsthat calculating a communication delay time t*_(com) according to theleading vehicle communication delay time t*_(com1) and the at least onefollowing vehicle communication delay time t*_(com2). The communicationdelay time t*_(com) is a set of the leading vehicle communication delaytime t*_(com1) and the at least one following vehicle communicationdelay time t*_(com2). Moreover, the step S0242 b is “Calculating vehicleplatoon control delay time”, and represents that calculating a controldelay time t*_(con) according to the leading vehicle control delay timet₁ ^(a) and the at least one following vehicle control delay time (t₂^(a)-t_(N) ^(a) The control delay time t*_(con) meets the followingequation (5):

t* _(con)=max{t ₁ ^(a) , . . . ,t _(N) ^(a)}  (5).

a represents an acceleration. In addition, the leading vehicle parametergroup 212 includes a vehicle speed V, a vehicle load m_(i) and a roadgradient. The step S0243 a is “Calculating vehicle platoon load responsetime”, and represents that calculating a load response time t_(m) ^(i)according to the vehicle speed V, an initial vehicle speed V₀, a vehicleexternal force F_(i) and the vehicle load m_(i). The step S0243 b is“Calculating vehicle platoon gradient changing time”, and representsthat calculating a gradient changing time t_(slo) ^(i) according to thevehicle speed V, the initial vehicle speed V₀, the vehicle externalforce F_(i), the vehicle load m_(i) and the road gradient. The loadresponse time t_(m) ^(i) and gradient changing time t_(slo) ^(i) arecalculated to obtain a vehicle platoon load gradient response time(t*_(m)+t*_(slo)). The vehicle platoon load gradient response time(t*_(m)+t*_(slo)) meets the following equations (6)-(10):

$\begin{matrix}{{t_{m}^{i} = \frac{V - V_{0}}{F_{i}/m_{i}}},{i = 1},2,\ldots,N,{{{if}{slope}} = 0.}} & (6)\end{matrix}$ $\begin{matrix}{{{t_{m}^{i} + t_{slo}^{i}} = \frac{V - V_{0}}{F_{i}/m_{i}}},{i = 1},2,\ldots,N,{{{if}{slope}} \neq 0.}} & (7)\end{matrix}$ $\begin{matrix}{F_{i} = {F_{T} - {\left( {F_{roll} + F_{grav} + F_{drg}} \right).}}} & (8)\end{matrix}$ $\begin{matrix}{{t_{m}^{*} = {{\max\left( t_{m}^{i} \right)} - {\min\left( t_{m}^{i} \right)}}},{{{and}t_{slo}^{*}} = 0},{{{if}{slope}} = 0.}} & (9)\end{matrix}$ $\begin{matrix}{{{t_{m}^{*} + t_{slo}^{*}} = {{\max\left( {t_{m}^{i} + t_{slo}^{i}} \right)} - {\min\left( {t_{m}^{i} + t_{slo}^{i}} \right)}}},{{{if}{slope}} \neq 0.}} & (10)\end{matrix}$

F_(T), F_(roll), F_(grav), F_(drg) and slope represent a driving force,a rolling resistance, a forward friction, an air resistance and the roadgradient, respectively. In addition, the step S0244 a is “Calculatingreaction time under uniformized range”, and represents that calculatinga plurality of reaction times t*_(a) _(min) , t*_(a) _(max) according tothe vehicle speed V, the initial vehicle speed V₀ and the uniformizedacceleration range parameter (a_(min), a_(max)). The reaction timest*_(a) _(min) , t*_(a) _(max) meet the following equation (11):

$\begin{matrix}{{V = {V_{0} + {at}}};\left\{ {\begin{matrix}{t_{a_{\min}}^{*} = \frac{V - V_{0}}{a_{\min}}} \\{t_{a_{\max}}^{*} = \frac{V - V_{0}}{a_{\max}}}\end{matrix}.} \right.} & (11)\end{matrix}$

The step S0244 b is “Calculating vehicle platoon total delay time”, andrepresents that calculating a total delay time range (T*_(min),T*_(max)) according to the reaction times t*_(a) _(min) , t*_(a) _(max), the control delay time t*_(con), the communication delay time t*_(com)and the vehicle platoon load gradient response time (t*_(m)+t*_(slo)).The total delay time range (T*_(min), T*_(max)) meets the followingequation (12):

$\begin{matrix}{\begin{bmatrix}T_{\max}^{*} \\T_{\min}^{*}\end{bmatrix} = {\begin{bmatrix}t_{a_{\max}}^{*} \\t_{a_{\min}}^{*}\end{bmatrix} + {\begin{bmatrix}{t_{con}^{*} + t_{com}^{*} + t_{m}^{*} + t_{slo}^{*}} \\{t_{con}^{*} + t_{com}^{*} + t_{m}^{*} + t_{slo}^{*}}\end{bmatrix}.}}} & (12)\end{matrix}$

The step S0244 c is “Adjusting acceleration range according to totaldelay time” and “(a_(pa), a_(min) ^(f), a_(max) ^(f))/(a_(min) ^(f),a_(max) ^(f))”, and represents that calculating the driving accelerationrange (a_(min) ^(f), a_(max) ^(f)) according to the vehicle speed V andthe total delay time range (T*_(min), T*_(max)). The drivingacceleration range (a_(min) ^(f), a_(max) ^(f)) meets the followingequation (13):

$\begin{matrix}{{V = {V_{0} + {at}}};\left\{ {\begin{matrix}{a_{\max}^{f} = \frac{V - V_{0}}{T_{\max}^{*}}} \\{a_{\min}^{f} = \frac{V - V_{0}}{T_{\min}^{*}}}\end{matrix}.} \right.} & (13)\end{matrix}$

In the step S0244 c, in response to determining that the driving modejudging result is that the leading vehicle 200 is in the autonomousmode, outputting the driving acceleration range (a_(min) ^(f), a_(max)^(f)). In response to determining that the driving mode judging resultis that the leading vehicle 200 is in the manual mode, outputting thedriving acceleration range (a_(min) ^(f), a_(max) ^(f)) and thecompensated acceleration a_(pa).

The step S0245 is “Using quadratic programming to solve simultaneousdifferential equation {dot over (x)}=Ax+Bu of vehicle platoon followingacceleration command”, and represents that setting the leading vehicleparameter group 212 and the vehicle speed limit, the road curvature anda vehicle platoon following time interval of the at least one followingvehicle parameter group 312 as a limiting condition, and using aquadratic programming (QP) to solve a simultaneous differential equation{dot over (x)}=Ax+Bu to generate a best solution of a vehicle platoonfollowing acceleration u. Parameter matrixes {dot over (X)}, A, x, B andthe vehicle platoon following acceleration u of the simultaneousdifferential equation meet the following equation (14). Ts represents asampling time. τ represents a control command response delay time, i.e.,a response time of the command from an engine to a wheel. xp(t)represents a location of a host vehicle. xv(t) represents a speed of thehost vehicle. xa(t) represents an acceleration of the host vehicle. u(t)represents an acceleration command. The detail of the QP is aconventional technology and will not be described again herein.

$\begin{matrix}{\begin{pmatrix}{{xp}\left( {t + {Ts}} \right)} \\{{xv}\left( {t + {Ts}} \right)} \\{{xa}\left( {t + {Ts}} \right)}\end{pmatrix} = {{\begin{pmatrix}1 & {Ts} & 0 \\0 & 1 & {Ts} \\0 & 0 & {1 - {{Ts}/\tau}}\end{pmatrix}\begin{pmatrix}{{xp}(t)} \\{{xv}(t)} \\{{xa}(t)}\end{pmatrix}} + {\begin{pmatrix}0 \\0 \\{{Ts}/\tau}\end{pmatrix}{{u(t)}.}}}} & (14)\end{matrix}$

The cloud acceleration estimating step S026 is performed to estimate thecompensated acceleration a_(pa) according to the leading vehicleparameter group 212. In detail, the cloud acceleration estimating stepS026 includes a plurality of steps S0262, S0264, S0266. The step S0262is “Executing front vehicle speed estimating module”, and representsthat transmitting the road curvature, the vehicle speed limit, thepercentage of throttle brake and the current acceleration of the frontvehicle of the leading vehicle parameter group 212 to a front vehiclespeed estimating module, and then calculating an estimated speed of thefront vehicle by the front vehicle speed estimating module according tothe road curvature, the vehicle speed limit, the percentage of throttlebrake and the current acceleration of the front vehicle. The step S0264is “Using Kalman filter model to predict acceleration of front vehicleat t>T (T=current time)”, and represents that using a Kalman filtermodel to compute the estimated speed of the front vehicle to generate anestimated acceleration of the front vehicle (i.e., the acceleration ofthe front vehicle at t>T). The step S0266 is “Estimating a_(pa)”, andrepresents that estimating the compensated acceleration a_(pa)corresponding the vehicle platoon according to the current accelerationof the front vehicle and the estimated acceleration of the frontvehicle.

The vehicle platoon acceleration calculating step S028 is performed tocalculate the vehicle platoon accelerations a_(f) according to thedriving mode judging result and at least one of the driving accelerationrange (a_(min) ^(f), a_(max) ^(f)) and the compensated accelerationa_(pa). In detail, in the vehicle platoon acceleration calculating stepS028, in response to determining that the driving mode judging result isthat the leading vehicle 200 is in the autonomous mode, the cloudprocessing unit 410 calculates the vehicle platoon accelerations a_(f)according to the driving acceleration range (a_(min) ^(f), a_(max) ^(f))and the vehicle platoon following acceleration u of the step S0245. Thevehicle platoon accelerations a_(f) meet the following equation (15):

$\begin{matrix}{a_{f} = \left\{ {\begin{matrix}{u,{{{if}u} \in \left\lbrack {a_{\min}^{f},a_{\max}^{f}} \right\rbrack}} \\{a_{\max}^{f},{{{if}u} > a_{\max}^{f}}} \\{a_{\min}^{f},{ow}}\end{matrix}.} \right.} & (15)\end{matrix}$

ow represents other conditions. In response to determining that thedriving mode judging result is that the leading vehicle 200 is in themanual mode, the cloud processing unit 410 calculates the vehicleplatoon accelerations a_(f) according to the driving acceleration range(a_(min) ^(f), a_(max) ^(f)) and the compensated acceleration a_(pa).The vehicle platoon accelerations a_(f) meet the following equation(16):

$\begin{matrix}{a_{f} = \left\{ {\begin{matrix}{a_{pa},{{{if}a_{pa}} \in \left\lbrack {a_{\min}^{f},a_{\max}^{f}} \right\rbrack}} \\{a_{\max}^{f},{{{if}a_{pa}} > a_{\max}^{f}}} \\{a_{\min}^{f},{ow}}\end{matrix}.} \right.} & (16)\end{matrix}$

Therefore, the vehicle platoon following deciding system 100 based oncloud computing of the present disclosure utilizes the cloud parameteruniformizing step S024 and the cloud acceleration estimating step S026to realize vehicle platoon following decisions in different drivingmodes, thereby not only effectively saving energy and increasing thesafety of the vehicle platoon, but also reducing the cost of hardwareand manpower.

Please refer to FIGS. 1, 2, 3, 4, 5 and 6 . FIG. 6 shows a schematicview of a vehicle platoon following deciding system 100 a based on cloudcomputing according to a second embodiment of the present disclosure.The vehicle platoon following deciding system 100 a based on cloudcomputing is configured to decide a plurality of vehicle platoonaccelerations a_(f) of a leading vehicle 200 and at least one followingvehicle 300, and includes the leading vehicle 200, a leading vehicleprocessing unit 210 a, a communicating device 220, a positioning device230, a sensing device 240, a warning device 250, the at least onefollowing vehicle 300, at least one following vehicle processing unit310 a, a communicating device 320, a positioning device 330, a sensingdevice 340, a warning device 350 and a cloud computing platform 400. Theleading vehicle 200, the communicating device 220, the positioningdevice 230, the sensing device 240, the at least one following vehicle300, the communicating device 320, the positioning device 330, thesensing device 340, a signal receiving step S11 and a cloud decidingstep S12 of the cloud processing unit 410 are the same as the leadingvehicle 200, the communicating device 220, the positioning device 230,the sensing device 240, the at least one following vehicle 300, thecommunicating device 320, the positioning device 330, the sensing device340, a signal receiving step S01 and a cloud deciding step S02 of thecloud processing unit 410 of FIG. 2 , respectively. In FIG. 6 , one ofthe leading vehicle processing unit 210 a and the at least one followingvehicle processing unit 310 a is configured to implement one of a delaydiagnosing step S14 a and at least one delay diagnosing step S14 b. Theone of the delay diagnosing step S14 a and the at least one delaydiagnosing step S14 b includes diagnosing whether a signal delay timebetween the cloud processing unit 410 and the one of the leading vehicleprocessing unit 210 a and the at least one following vehicle processingunit 310 a is smaller than or equal to a predetermined delay time togenerate a delay diagnosis result. The warning devices 250, 350 aresignally connected to the leading vehicle processing unit 210 a and theat least one following vehicle processing unit 310 a, respectively. Oneof the warning devices 250, 350 determines whether to provide a warningsignal according to the delay diagnosis result.

The leading vehicle processing unit 210 a is configured to implement thedelay diagnosing step S14 a. The delay diagnosing step S14 a includesdiagnosing whether the signal delay time between the cloud processingunit 410 and the leading vehicle processing unit 210 a is smaller thanor equal to the predetermined delay time to generate the delay diagnosisresult. In detail, the delay diagnosing step S14 a further includesconfiguring the leading vehicle processing unit 210 a to receive thevehicle platoon accelerations a_(f). The leading vehicle processing unit210 a is corresponding to the leading vehicle 200. In response todetermining that the delay diagnosis result is that the signal delaytime is smaller than or equal to the predetermined delay time,performing a step S16 a. The step S16 a is “Controlling longitudinalacceleration”, and represents that configuring the leading vehicleprocessing unit 210 a to control an acceleration of the leading vehicle200 according to the vehicle platoon accelerations a_(f). In response todetermining that the delay diagnosis result is that the signal delaytime is greater than the predetermined delay time, performing a step S18a. The step S18 a is “Warning driver to intervene”, and represents thatconfiguring the warning device 250 to provide the warning signal forwarning a driver to intervene.

The at least one following vehicle processing unit 310 a is configuredto implement the at least one delay diagnosing step S14 b. The at leastone delay diagnosing step S14 b includes diagnosing whether the signaldelay time between the cloud processing unit 410 and the at least onefollowing vehicle processing unit 310 a is smaller than or equal to thepredetermined delay time to generate the delay diagnosis result. Indetail, the delay diagnosing step S14 b further includes configuring theat least one following vehicle processing unit 310 a to receive thevehicle platoon accelerations a_(f). The at least one following vehicleprocessing unit 310 a is corresponding to the at least one followingvehicle 300. In response to determining that the delay diagnosis resultis that the signal delay time is smaller than or equal to thepredetermined delay time, performing a step S16 b. The step S16 b is“Controlling longitudinal acceleration”, and represents that configuringthe at least one following vehicle processing unit 310 a to control anacceleration of the at least one following vehicle 300 according to thevehicle platoon accelerations a_(f). In response to determining that thedelay diagnosis result is that the signal delay time is greater than thepredetermined delay time, performing a step S18 b. The step S18 b is“Warning driver to intervene”, and represents that configuring thewarning device 350 to provide the warning signal for warning the driverto intervene.

Please refer to FIGS. 6, 7 and 8 . FIG. 7 shows a schematic view of asignal delay time T of one of plurality of delay diagnosing steps S14 a,S14 b of FIG. 6 , corresponding to a signal path. FIG. 8 shows a flowchart of estimation of the signal delay time T of the one of the delaydiagnosing steps S14 a, S14 b of FIG. 6 . The signal delay time T isequal to a sum of a signal sending time T_(Send), a signal computingtime T_(Compute) and a signal receiving time T_(Receive). The signalsending time T_(Send) represents the time required for a signal to betransmitted from a vehicle end to a cloud end. The signal computing timeT_(Compute) represents the time required for the signal to be computedin the cloud end. The signal receiving time T_(Receive) represents thetime required for the signal to be transmitted from the cloud end to thevehicle end. The vehicle end may correspond to the leading vehicle 200or the at least one following vehicle 300 in FIG. 1 , and the cloud endmay correspond to the cloud computing platform 400 in FIG. 1 . Inaddition, the signal delay time T can be obtained by calculating asending parameter Sindex and a receiving parameter Rindex, as shown inFIG. 8 . Specifically, the vehicle end transmits the sending parameterSindex (e.g., the sending parameter Sindex=1), the receiving parameterRindex (e.g., the receiving parameter Rindex=1) and the number of delaysN_(i) (e.g., an initial value of the number of delays N=1) to the cloudend, and then the cloud end returns “Rindex=Sindex+1” to the vehicleend. The vehicle end confirms whether the sending parameter Sindex isnot equal to the receiving parameter Rindex in a fixed period of time(e.g., 100 ms). If yes (i.e., the sending parameter Sindex is not equalto the receiving parameter Rindex), the vehicle end calculates“T=N_(i)×(100 ms)” and diagnoses whether the signal delay time T issmaller than or equal to the predetermined delay time, and then executes“N_(i)=1”. If no (i.e., the sending parameter Sindex is equal to thereceiving parameter Rindex), the vehicle end calculates “T=N_(i)×(100ms)” and diagnoses whether the signal delay time T is smaller than orequal to the predetermined delay time, and then executes“N_(i)=N_(i)+1”. The predetermined delay time can be set according torequirements. In one embodiment, the predetermined delay time may be 300ms, but the present disclosure is not limited thereto.

Therefore, the vehicle platoon following deciding system 100 a based oncloud computing of the present disclosure utilizes the delay diagnosingsteps S14 a, S14 b to confirm whether the signal delay time T is withinthe allowable predetermined delay time to realize a delayself-diagnosis. If the signal delay time T is not within the allowablepredetermined delay time, the vehicle platoon following deciding system100 a provides the warning signal to protect the safety of the vehicleplatoon.

Please refer to FIGS. 1, 2, 3 and 9 . FIG. 9 shows a flow chart of adeciding method 500 of a vehicle platoon following deciding system 100based on cloud computing according to a third embodiment of the presentdisclosure. The deciding method 500 of the vehicle platoon followingdeciding system 100 based on cloud computing is configured to decide aplurality of vehicle platoon accelerations a_(f) of a leading vehicle200 and at least one following vehicle 300, and includes performing acloud deciding step S02. The cloud deciding step S02 includes performinga driving mode judging step S022, a cloud parameter uniformizing stepS024, a cloud acceleration estimating step S026 and a vehicle platoonacceleration calculating step S028. The driving mode judging step S022is performed to configure a cloud processing unit 410 to judge whetherthe leading vehicle 200 is manually driven according to a leadingvehicle parameter group 212 to generate a driving mode judging result.The cloud parameter uniformizing step S024 includes configuring thecloud processing unit 410 to calculate a driving acceleration range(a_(min) ^(f), a_(max) ^(f)) according to the leading vehicleacceleration range (a_(L) ¹, a_(U) ¹) and the at least one followingvehicle acceleration range (a_(L) ², a_(U) ²)-(a_(L) ^(N), a_(U) ^(N)).The cloud acceleration estimating step S026 is performed to configurethe cloud processing unit 410 to estimate a compensated accelerationa_(pa) according to the leading vehicle parameter group 212. The vehicleplatoon acceleration calculating step S028 is performed to configure thecloud processing unit 410 to calculate the vehicle platoon accelerationsa_(f) according to the driving mode judging result and at least one ofthe driving acceleration range (a_(min) ^(f), a_(max) ^(f)) and thecompensated acceleration a_(pa).

Please refer to FIGS. 6 and 10 . FIG. 10 shows a flow chart of adeciding method 500 a of a vehicle platoon following deciding system 100a based on cloud computing according to a fourth embodiment of thepresent disclosure. The deciding method 500 a is applied to the vehicleplatoon following deciding system 100 a based on cloud computing, andincludes performing a cloud deciding step S12 and a delay diagnosingstep S14. The cloud deciding step S12 includes performing a driving modejudging step S122, a cloud parameter uniformizing step S124, a cloudacceleration estimating step S126 and a vehicle platoon accelerationcalculating step S128. The cloud deciding step S12 is the same as thecloud deciding step S12 of FIG. 6 . The delay diagnosing step S14corresponds to one of the delay diagnosing steps S14 a, S14 b of FIG. 6. The delay diagnosing step S14 includes diagnosing whether a signaldelay time T between the cloud processing unit 410 and one of a leadingvehicle processing unit 210 a and at least one following vehicleprocessing unit 310 a is smaller than or equal to a predetermined delaytime to generate a delay diagnosis result. Therefore, the decidingmethod 500 a of the vehicle platoon following deciding system 100 abased on cloud computing of the present disclosure utilizes the delaydiagnosing step S14 to confirm whether the signal delay time T is withinthe allowable predetermined delay time to realize a delayself-diagnosis. If the signal delay time T is not within the allowablepredetermined delay time, the vehicle platoon following deciding system100 a provides the warning signal to protect the safety of the vehicleplatoon.

Please refer to FIGS. 6, 11 and 12 . FIG. 11 shows a schematic view of avehicle platoon following deciding system based on cloud computing forprocessing a scene with an obstacle 600 cut-in according to a fifthembodiment of the present disclosure. FIG. 12 shows a flow chart of adeciding method of a vehicle platoon following deciding system based oncloud computing for processing a scene with an obstacle 600 cut-inaccording to a sixth embodiment of the present disclosure. The vehicleplatoon following deciding system based on cloud computing includes atleast one of the sensing devices 240, 340. The at least one of thesensing devices 240, 340 is disposed on one of the leading vehicle 200and the at least one following vehicle 300, and signally connected toone of the leading vehicle processing unit 210 a and the at least onefollowing vehicle processing unit 310 a. The at least one of the sensingdevices 240, 340 is configured to sense a surrounding environment of theone of the leading vehicle 200 and the at least one following vehicle300 to judge whether there is at least one obstacle 600 between theleading vehicle 200 and the at least one following vehicle 300.

The deciding method of the vehicle platoon following deciding systembased on cloud computing includes performing an obstacle cut-in decidingstep S13. The obstacle cut-in deciding step S13 is performed toconfigure the at least one of the sensing devices 240, 340 to sense thesurrounding environment of the one of the leading vehicle 200 and the atleast one following vehicle 300 to judge whether there is the at leastone obstacle 600 between the leading vehicle 200 and the at least onefollowing vehicle 300. In response to determining that there is the atleast one obstacle 600 between the leading vehicle 200 and the at leastone following vehicle 300, the at least one of the sensing devices 240,340 generates at least one relative obstacle distance and at least oneobstacle speed, and the one of the leading vehicle processing unit 210 aand the at least one following vehicle processing unit 310 a transmitsthe at least one relative obstacle distance and the at least oneobstacle speed to the cloud processing unit 410, so that the cloudprocessing unit 410 analyzes that the one of the leading vehicle 200 andthe at least one following vehicle 300 is in one of a safe state SS andan emergency state ES. In detail, the obstacle cut-in deciding step S13includes a plurality of steps S131, S132, S133, S134, S135, S136, S137,S138, S139, S1310, S1311. The step S131 is “Can following vehicle senseenvironment?”, and represents that confirming whether the at least onefollowing vehicle 300 can sense the surrounding environment. If yes(i.e., the sensing device 340 is disposed on the at least one followingvehicle 300), performing the step S132. If no, performing the step S133.The step S132 is “Is there obstacle in vehicle platoon?”, and representsthat confirming whether there is the at least one obstacle 600 in thevehicle platoon. If yes, performing the step S135. If no, ending theobstacle cut-in deciding step S13. The step S133 is “Is obstacle withinsensing range?”, and represents that confirming whether the at least oneobstacle 600 is within a sensing range of the leading vehicle 200 or asensing range of a roadside detection device. If yes, performing thestep S132. If no, performing the step S134. The step S134 is “Doesfollowing vehicle have driver?”, and represents that confirming whetherthe at least one following vehicle 300 has a driver. If yes, warning thedriver to pay attention to front intervention at any time. If no, endingthe obstacle cut-in deciding step S13. The step S135 is “Detectingcollision (assuming i is nearest member in front of obstacle)”, andrepresents that configuring the cloud processing unit 410 analyzes thatthe at least one following vehicle 300 is in one of the safe state SSand the emergency state ES according to a relative obstacle distance, acommunication position, a road curvature, a road gradient, a relativespeed, a chassis response and a vehicle load. The communication positionincludes a communication delay and a current coordinate. The relativespeed represents the relative speed between the at least one obstacle600 and the vehicle end.

Furthermore, assuming that the number of at least one obstacle 600 andthe number of the at least one obstacle speed are both plural. Inresponse to determining that the at least one following vehicle 300 isin the safe state SS, performing the step S136. The step S136 is “Isobstacle dynamic?”, and represents that confirming whether the obstaclespeeds of the obstacles 600 are greater than 0 m/s. If yes (i.e., theobstacle speeds corresponding to the obstacles 600 are all greater than0 m/s), performing the step S137. If no, performing the step S139. Inresponse to determining that the at least one following vehicle 300 isin the emergency state ES, performing the steps S138, S139. The stepS137 is “Limiting front obstacle TimeGap” and “Limiting rear obstacleTimeGap”, and represents that configuring the cloud processing unit 410to further analyze a front obstacle time interval (corresponding to“Limiting front obstacle TimeGap”) and a rear obstacle time interval(corresponding to “Limiting rear obstacle TimeGap”) between the at leastone following vehicle 300 and the obstacles 600 to generate safetycompliance following decisions under the condition of the obstacles 600cut-in (belonging to a multi cut-in scene). In addition, the step S138is “Emergency braking for i+1th, . . . , Nth vehicle”, and representsthat configuring the i+1 th to Nth following vehicles 300 to emergencybrake. For example, in FIG. 11 , i is equal to 1 (the nearest member ofthe vehicle platoon in front of the obstacle 600 is the leading vehicle200), and the step S138 is configuring the 2nd to Nth following vehicles300 to emergency brake. The step S139 is “Confirming obstructedfollowing vehicles within remote control range or having driver?”, andrepresents that confirming whether the obstructed following vehicles 300(i.e., the i+1th to Nth following vehicles 300) are within a remotecontrol range or has a driver. If yes, performing the step S1310. If no,performing the step S1311. The step S1310 is “Restarting vehicle platoonfollowing after obstacle avoidance”, and represents that controlling theobstructed following vehicles 300 (i.e., the i+1th to Nth followingvehicles 300) to avoid the obstacle 600 by a remote end or the driver,and restarting the obstructed following vehicles 300 to follow theleading vehicle 200 after the obstacle avoidance. The step S1311 is“Releasing all autonomous vehicles to stop and waiting for rescue”, andrepresents that controlling the leading vehicle 200 and the at least onefollowing vehicle 300 to stop and waiting for rescue.

Therefore, the deciding method of the vehicle platoon following decidingsystem based on cloud computing of the present disclosure processes ascene with the obstacles 600 cut-in by the obstacle cut-in deciding stepS13 and consider a front vehicle time interval and a rear vehicle timeinterval (i.e., the front obstacle time interval and the rear obstacletime interval) at the same time, thus avoiding serious consequences(e.g., the collision and the vehicle accident) without considering therear obstacle time interval in a conventional technology, and greatlyincreasing the overall safety of the vehicle platoon when the obstacles600 cut-in.

In other embodiments, the cloud processing unit 410 may be disposed onthe leading vehicle 200, or combined with the leading vehicle processingunit 210 to be disposed on the leading vehicle 200. In other words, thevehicle platoon following decision can be performed by the leadingvehicle 200 (i.e., the vehicle end) instead of the cloud end. Inaddition, each of the leading vehicle processing units 210, 210 a, thefollowing vehicle processing units 310, 310 a and the cloud processingunit 410 of the present disclosure may be a microprocessor, anelectronic control unit (ECU), a computer, a mobile device or othercomputing processors, but the present disclosure is not limited thereto.Moreover, the driver of the leading vehicle 200 and the at least onefollowing vehicle 300 may be optional. If the leading vehicle 200 is inthe manual mode, the leading vehicle 200 has the driver. The roadsidedetection device may be disposed on the driving path in the vehicleplatoon following deciding system according to requirements. If theroadside detection device is disposed in the vehicle platoon followingdeciding system, the roadside detection device is configured to transmita roadside detected signal to the cloud processing unit 410 forsubsequent judgment and analysis.

According to the aforementioned embodiments and examples, the advantagesof the present disclosure are described as follows.

1. The vehicle platoon following deciding system based on cloudcomputing and the deciding method thereof of the present disclosureutilize the cloud parameter uniformizing step and the cloud accelerationestimating step to realize the vehicle platoon following decisions andthe multi obstacles cut-in decisions in different driving modes.

2. The vehicle platoon following deciding system based on cloudcomputing and the deciding method thereof of the present disclosureutilize the delay diagnosing steps to confirm whether the signal delaytime is within the allowable predetermined delay time to realize a delayself-diagnosis. In addition, the present disclosure realizes the vehicleplatoon following decision, the multi obstacles cut-in decision and thedelay self-diagnosis by the combination of the cloud deciding step ofthe cloud end and the delay diagnosing step of the vehicle end, therebynot only effectively saving energy and increasing the safety of thevehicle platoon, but also reducing the cost of hardware and manpower.

3. The vehicle platoon following deciding system based on cloudcomputing and the deciding method thereof of the present disclosureprocess a scene with the obstacles cut-in by the obstacle cut-indeciding step and consider the front obstacle time interval and the rearobstacle time interval at the same time, thus avoiding seriousconsequences without considering the rear obstacle time interval in aconventional technology, and greatly increasing the overall safety ofthe vehicle platoon when the obstacles cut-in.

Although the present disclosure has been described in considerabledetail with reference to certain embodiments thereof, other embodimentsare possible. Therefore, the spirit and scope of the appended claimsshould not be limited to the description of the embodiments containedherein.

It will be apparent to those skilled in the art that variousmodifications and variations can be made to the structure of the presentdisclosure without departing from the scope or spirit of the disclosure.In view of the foregoing, it is intended that the present disclosurecover modifications and variations of this disclosure provided they fallwithin the scope of the following claims.

What is claimed is:
 1. A vehicle platoon following deciding system basedon cloud computing, which is configured to decide a plurality of vehicleplatoon accelerations of a leading vehicle and at least one followingvehicle, the vehicle platoon following deciding system based on cloudcomputing comprising: a leading vehicle processing unit disposed on theleading vehicle and configured to generate a leading vehicle parametergroup, wherein the leading vehicle parameter group comprises a leadingvehicle acceleration range; at least one following vehicle processingunit disposed on the at least one following vehicle and configured togenerate at least one following vehicle parameter group, wherein the atleast one following vehicle parameter group comprises at least onefollowing vehicle acceleration range; and a cloud processing unitsignally connected to the leading vehicle processing unit and the atleast one following vehicle processing unit, and receiving the leadingvehicle parameter group and the at least one following vehicle parametergroup, wherein the cloud processing unit is configured to implement acloud deciding step comprising: performing a driving mode judging stepto judge whether the leading vehicle is manually driven according to theleading vehicle parameter group to generate a driving mode judgingresult; performing a cloud parameter uniformizing step comprisingcalculating a driving acceleration range according to the leadingvehicle acceleration range and the at least one following vehicleacceleration range; performing a cloud acceleration estimating step toestimate a compensated acceleration according to the leading vehicleparameter group; and performing a vehicle platoon accelerationcalculating step to calculate the vehicle platoon accelerationsaccording to the driving mode judging result and at least one of thedriving acceleration range and the compensated acceleration.
 2. Thevehicle platoon following deciding system based on cloud computing ofclaim 1, wherein in the vehicle platoon acceleration calculating step,in response to determining that the driving mode judging result is thatthe leading vehicle is in an autonomous mode, the cloud processing unitcalculates the vehicle platoon accelerations according to the drivingacceleration range; and in response to determining that the driving modejudging result is that the leading vehicle is in a manual mode, thecloud processing unit calculates the vehicle platoon accelerationsaccording to the driving acceleration range and the compensatedacceleration.
 3. The vehicle platoon following deciding system based oncloud computing of claim 1, wherein, the leading vehicle processing unitis configured to generate a leading vehicle control delay time and aleading vehicle communication delay time; the at least one followingvehicle processing unit is configured to generate at least one followingvehicle control delay time and at least one following vehiclecommunication delay time; and the cloud parameter uniformizing stepfurther comprises: calculating a control delay time according to theleading vehicle control delay time and the at least one followingvehicle control delay time; and calculating a communication delay timeaccording to the leading vehicle communication delay time and the atleast one following vehicle communication delay time.
 4. The vehicleplatoon following deciding system based on cloud computing of claim 3,wherein, the leading vehicle parameter group comprises a vehicle speed,a vehicle load and a road gradient; and the cloud parameter uniformizingstep further comprises: calculating a vehicle platoon load gradientresponse time according to the vehicle speed, the vehicle load and theroad gradient.
 5. The vehicle platoon following deciding system based oncloud computing of claim 4, wherein the cloud parameter uniformizingstep further comprises: calculating a uniformized acceleration rangeparameter according to the leading vehicle acceleration range and the atleast one following vehicle acceleration range; and calculating a totaldelay time range according to the vehicle speed, the uniformizedacceleration range parameter, the control delay time, the communicationdelay time and the vehicle platoon load gradient response time, and thencalculating the driving acceleration range according to the vehiclespeed and the total delay time range.
 6. The vehicle platoon followingdeciding system based on cloud computing of claim 1, wherein, theleading vehicle parameter group comprises a road curvature, a percentageof throttle brake and a current acceleration of a front vehicle; and thecloud acceleration estimating step comprises: estimating the compensatedacceleration by calculating the road curvature, the percentage ofthrottle brake and the current acceleration of the front vehicleaccording to a Kalman filter model.
 7. The vehicle platoon followingdeciding system based on cloud computing of claim 1, further comprising:a sensing device disposed on one of the leading vehicle and the at leastone following vehicle, and signally connected to one of the leadingvehicle processing unit and the at least one following vehicleprocessing unit, wherein the sensing device is configured to sense asurrounding environment of the one of the leading vehicle and the atleast one following vehicle to judge whether there is at least oneobstacle between the leading vehicle and the at least one followingvehicle; wherein in response to determining that there is the at leastone obstacle between the leading vehicle and the at least one followingvehicle, the sensing device generates at least one relative obstacledistance and at least one obstacle speed, and the one of the leadingvehicle processing unit and the at least one following vehicleprocessing unit transmits the at least one relative obstacle distanceand the at least one obstacle speed to the cloud processing unit, sothat the cloud processing unit analyzes that the at least one followingvehicle is in one of a safe state and an emergency state.
 8. The vehicleplatoon following deciding system based on cloud computing of claim 7,wherein a number of the at least one obstacle and a number of the atleast one obstacle speed are both plural; wherein in response todetermining that the cloud processing unit analyzes that the at leastone following vehicle is in the safe state, and the obstacle speedscorresponding to the obstacles are all greater than 0 m/s, the cloudprocessing unit further analyzes a front obstacle time interval and arear obstacle time interval between the at least one following vehicleand the obstacles; wherein in response to determining that the cloudprocessing unit analyzes that the at least one following vehicle is inthe emergency state, the cloud processing unit controls the at least onefollowing vehicle to perform braking.
 9. A vehicle platoon followingdeciding system based on cloud computing, which is configured to decidea plurality of vehicle platoon accelerations of a leading vehicle and atleast one following vehicle, the vehicle platoon following decidingsystem based on cloud computing comprising: a leading vehicle processingunit disposed on the leading vehicle and configured to generate aleading vehicle parameter group, wherein the leading vehicle parametergroup comprises a leading vehicle acceleration range; at least onefollowing vehicle processing unit disposed on the at least one followingvehicle and configured to generate at least one following vehicleparameter group, wherein the at least one following vehicle parametergroup comprises at least one following vehicle acceleration range; and acloud processing unit signally connected to the leading vehicleprocessing unit and the at least one following vehicle processing unit,and receiving the leading vehicle parameter group and the at least onefollowing vehicle parameter group, wherein the cloud processing unit isconfigured to implement a cloud deciding step comprising generating adriving mode judging result, a driving acceleration range and acompensated acceleration according to the leading vehicle parametergroup, the leading vehicle acceleration range and the at least onefollowing vehicle acceleration range, and then calculating the vehicleplatoon accelerations according to the driving mode judging result andat least one of the driving acceleration range and the compensatedacceleration; wherein one of the leading vehicle processing unit and theat least one following vehicle processing unit is configured toimplement a delay diagnosing step, the delay diagnosing step comprisesdiagnosing whether a signal delay time between the cloud processing unitand the one of the leading vehicle processing unit and the at least onefollowing vehicle processing unit is smaller than or equal to apredetermined delay time to generate a delay diagnosis result.
 10. Thevehicle platoon following deciding system based on cloud computing ofclaim 9, further comprising: a warning device signally connected to theone of the leading vehicle processing unit and the at least onefollowing vehicle processing unit, wherein the warning device determineswhether to provide a warning signal according to the delay diagnosisresult.
 11. The vehicle platoon following deciding system based on cloudcomputing of claim 10, wherein the delay diagnosing step furthercomprises: configuring the one of the leading vehicle processing unitand the at least one following vehicle processing unit to receive thevehicle platoon accelerations, wherein the one of the leading vehicleprocessing unit and the at least one following vehicle processing unitis corresponding to one of the leading vehicle and the at least onefollowing vehicle; in response to determining that the delay diagnosisresult is that the signal delay time is smaller than or equal to thepredetermined delay time, configuring the one of the leading vehicleprocessing unit and the at least one following vehicle processing unitto control an acceleration of the one of the leading vehicle and the atleast one following vehicle according to the vehicle platoonaccelerations; and in response to determining that the delay diagnosisresult is that the signal delay time is greater than the predetermineddelay time, configuring the warning device to provide the warningsignal.
 12. The vehicle platoon following deciding system based on cloudcomputing of claim 9, wherein the cloud deciding step further comprises:performing a driving mode judging step to judge whether the leadingvehicle is manually driven according to the leading vehicle parametergroup to generate the driving mode judging result; performing a cloudparameter uniformizing step comprising calculating the drivingacceleration range according to the leading vehicle acceleration rangeand the at least one following vehicle acceleration range; andperforming a cloud acceleration estimating step to estimate thecompensated acceleration according to the leading vehicle parametergroup.
 13. The vehicle platoon following deciding system based on cloudcomputing of claim 9, wherein in the cloud deciding step, in response todetermining that the driving mode judging result is that the leadingvehicle is in an autonomous mode, the cloud processing unit calculatesthe vehicle platoon accelerations according to the driving accelerationrange; and in response to determining that the driving mode judgingresult is that the leading vehicle is in a manual mode, the cloudprocessing unit calculates the vehicle platoon accelerations accordingto the driving acceleration range and the compensated acceleration. 14.The vehicle platoon following deciding system based on cloud computingof claim 12, wherein, the leading vehicle processing unit is configuredto generate a leading vehicle control delay time and a leading vehiclecommunication delay time; the at least one following vehicle processingunit is configured to generate at least one following vehicle controldelay time and at least one following vehicle communication delay time;and the cloud parameter uniformizing step further comprises: calculatinga control delay time according to the leading vehicle control delay timeand the at least one following vehicle control delay time; andcalculating a communication delay time according to the leading vehiclecommunication delay time and the at least one following vehiclecommunication delay time.
 15. The vehicle platoon following decidingsystem based on cloud computing of claim 14, wherein, the leadingvehicle parameter group comprises a vehicle speed, a vehicle load and aroad gradient; and the cloud parameter uniformizing step furthercomprises: calculating a vehicle platoon load gradient response timeaccording to the vehicle speed, the vehicle load and the road gradient.16. The vehicle platoon following deciding system based on cloudcomputing of claim 15, wherein the cloud parameter uniformizing stepfurther comprises: calculating a uniformized acceleration rangeparameter according to the leading vehicle acceleration range and the atleast one following vehicle acceleration range; and calculating a totaldelay time range according to the vehicle speed, the uniformizedacceleration range parameter, the control delay time, the communicationdelay time and the vehicle platoon load gradient response time, and thencalculating the driving acceleration range according to the vehiclespeed and the total delay time range.
 17. The vehicle platoon followingdeciding system based on cloud computing of claim 12, wherein, theleading vehicle parameter group comprises a road curvature, a percentageof throttle brake and a current acceleration of a front vehicle; and thecloud acceleration estimating step comprises: estimating the compensatedacceleration by calculating the road curvature, the percentage ofthrottle brake and the current acceleration of the front vehicleaccording to a Kalman filter model.
 18. A deciding method of a vehicleplatoon following deciding system based on cloud computing, which isconfigured to decide a plurality of vehicle platoon accelerations of aleading vehicle and at least one following vehicle, the deciding methodof the vehicle platoon following deciding system based on cloudcomputing comprising: performing a cloud deciding step comprising:performing a driving mode judging step to configure a cloud processingunit to judge whether the leading vehicle is manually driven accordingto a leading vehicle parameter group to generate a driving mode judgingresult; performing a cloud parameter uniformizing step comprisingconfiguring the cloud processing unit to calculate a drivingacceleration range according to a leading vehicle acceleration range andat least one following vehicle acceleration range; performing a cloudacceleration estimating step to configure the cloud processing unit toestimate a compensated acceleration according to the leading vehicleparameter group; and performing a vehicle platoon accelerationcalculating step to configure the cloud processing unit to calculate thevehicle platoon accelerations according to the driving mode judgingresult and at least one of the driving acceleration range and thecompensated acceleration.
 19. The deciding method of the vehicle platoonfollowing deciding system based on cloud computing of claim 18, furthercomprising: performing a delay diagnosing step comprising diagnosingwhether a signal delay time between the cloud processing unit and one ofa leading vehicle processing unit and at least one following vehicleprocessing unit is smaller than or equal to a predetermined delay time;wherein the leading vehicle processing unit and the at least onefollowing vehicle processing unit are disposed on the leading vehicleand the at least one following vehicle, respectively.
 20. The decidingmethod of the vehicle platoon following deciding system based on cloudcomputing of claim 19, wherein the cloud deciding step furthercomprises: performing an obstacle cut-in deciding step to configure asensing device to sense a surrounding environment of one of the leadingvehicle and the at least one following vehicle to judge whether there isat least one obstacle between the leading vehicle and the at least onefollowing vehicle; wherein in response to determining that there is theat least one obstacle between the leading vehicle and the at least onefollowing vehicle, the sensing device generates at least one relativeobstacle distance and at least one obstacle speed, and the one of theleading vehicle processing unit and the at least one following vehicleprocessing unit transmits the at least one relative obstacle distanceand the at least one obstacle speed to the cloud processing unit, sothat the cloud processing unit analyzes that the one of the leadingvehicle and the at least one following vehicle is in one of a safe stateand an emergency state.